on 04-May-2017 (Thu)

1. Introduction

2. Dynamic systems theory

3. The A-not-B error

4. Generalization of names for hierarchical categories

5. Using space to infer reference

6. Conclusion

We argue that, compared to other approaches, the focus on time and integration across system components that are central to dynamic systems theory (DST) leads to different answers to Marr’s questions and, critically, suggests that starting with the computational- level analysis can lead to serious misunderstandings of behavior

DST focuses on the processes of change over time in complex systems. It views behav- ior, including cognition, as emerging from the interaction of multiple softly assembled com- ponents that are mutually influential and evolve over multiple embedded timescales

For example, the particular muscles and joint angles used to pick up your coffee cup will change based on many factors, including the starting point of the reach, the introduction of obstacles in the path, or the weight of a new watch on your wrist

Mutual interactivity means interactions proceed in both directions; not only does atten- tional selection influence the contents of visual working memory, but the contents of visual working memory reciprocally influence selective attention

Viewing timescales as embedded means appreciating that the dif- ferent timescales cognitive science often considers are not independent and cannot be stud- ied without recourse to each other.

preciating that behavior is emergent and softly assembled means understanding that behavior is the product of multiple components brought together in a moment of time based on the particular context, task, and history of the organism. The interaction of these components is not pre-specified or deterministic; thus, the particular assembly and resulting behavior are unique and variable from moment to moment and across specific contexts.

DFT is an embodied, dynamic systems approach to cognitive-level processes based on an understanding of brain function at the level of neural population dynamics

The basic building block of for- mal dynamic neural field (DNF) models is a field of metrically organized neurons; that is, neurons in this field are structured such that those close together have receptive fields that respond to similar feature values while those farther apart have receptive fields tuned to very different feature values

Whereas DST provides a set of theoretical tools, mathematical formalizations, and empirical approaches for understanding complex systems (Lewis & Granic, 2000; Newell & Molenaar, 1998; Port & van Gelder, 1995; Thelen & Smith, 1994), DFT adds a formal approach for analyzing and understanding cognitive-level processes. Furthermore, because DFT links cognitive-level processes to both neural population dynamics and behavior, and focuses on how behavior evolves over time, it explicitly integrates brain and behavior and provides a formal method for understanding both behavior and behavior change.

Thus, the computational analysis suggested that the task was to remember that something existed even when it is out of sight (see for discussion McClelland et al., 2010; Smith & Thelen, 2003; Thelen & Smith, 1994), and the behavioral change was explained at the algorithmic and representational level in terms of a deficit in cognitive processing. Ten- month-old infants, who make the error reliably, were said to lack a specific representation called the “object concept” (Piaget, 1954) and did not understand that the object contin- ued to exist after it was hidden. In contrast, 12-month-old infants, who are typically able to search correctly, were said to represent the object mentally even when it was not visu- ally available.

In contrast, a DST analysis of this phenomenon revealed critical influences of the motor system and body by recognizing the role of infants’ ability to stabilize and repeat a reach to the same location

Clearly, the A-not-B error cannot be about a failure to represent an object’s existence if the object is in full view. Rather, DST behavioral analysis suggests that changes in motor components at two different timescales affected behavioral change: reaches made over the course of an experiment and motor stability over the course of development. Thus, this work dem- onstrates that the behavior is not just about cognitive-level processes but about the body as well—the representation of things in the world is tightly coupled to the dynamics of the body

counter this argument, we next turn to recent research on adults’ and children’s acquisition of names for hierarchical categories. This program of work has applied a DST perspective to a phenomenon—the suspicious coincidence effect (SCE)—previously thought to reflect abstract reasoning about category membership. A dynamic systems approach has integrated the SCE with the trajectory of vocabulary development and with lower level processes such as perceptual comparison and working memory. In addition, this perspective grounds the SCE in neural population dynamics via DFT. 4. Generalization of names for hierarchical categories The challenge in learning names for hierarchically nested categories is in inferring the correct level of application of a novel word based on limited exposure.

Xu and Tenenbaum explicitly separated Marr’s levels in their approach, stating, “Our analysis of word learning focuses on what Marr (1982) called the level of computational theory. We have tried to elucidate the logic behind word learners’ inductive inferences, without specifying how that logic is imple- mented algorithmically in the mind or physiologically in neural hardware”

suspicious coincidence effect

Subsequent research, however, suggests that details of the neural and behavioral process by which novel names are perceived, remembered, and generalized matter greatly in the SCE, and change our understanding of the computational-level task.

Based on this prior work then, Spencer et al. (2011) presented adults with either three subordinate-level exemplars at once or sequentially and found that that the SCE was eliminated in the sequential condition. Furthermore, Spencer et al. (2011) were able to eliminate the SCE entirely by presenting six exemplars—an amount predicted to over- whelm working memory—sequentially.

According to the Bayesian account, more knowl- edge should lead to a stronger SCE. In contrast, Jenkins, Samuelson, Smith, and Spencer (2014; see also Samuelson, Spencer & Jenkins, 2013) showed that children who had less knowledge of the familiar categories used in Xu and Tenenbaum’s task showed a strong SCE, whereas children entering with more knowledge of the English categories showed no effect. Thus, the suspicious coincidence follows a non-linear, U-shaped curve over the developmental timescale of category learning.

A central question in the field of early word learning is how is it that young learners are so good at inferring the meaning of novel words, given that the possible referents in a naming situation can range from the objects present in the visual scene to properties of those objects, ongoing actions, and so on

object is presented on the other side of the table and the child is again allowed to reach for, grasp, and explore the object. This is repeated for a set of familiarization trials. Both objects are then placed in separate opaque buckets on either side of the table. The experi- menter looks into one bucket and says “Modi!” The object from the other bucket is then taken out and placed on its side of the table. It is removed after the child examines it, and the other object is placed on the table. After examination, this item is also removed. Both objects are then placed on a tray on the center of the table. The tray is pushed toward the child, and the experimenter asks, “Can you get me the modi?” Children retrieve the object that was in the bucket the experimenter was looking in when she said the novel word 0.70 of the time. Baldwin interpreted this result as suggesting children understood the pragmatic use of eye gaze as an intentional cue

That is, during the familiarization trials, the children’s behaviors—looking at the objects, reaching for them, manipulating them, and attending as each is removed—create associations between each of the novel objects and their unique locations in the space of the task. Thus, when the experimenter looks into a bucket placed at one of those unique locations and says the name, the child’s memory of the object previously seen and acted on at that location is recalled and bound to the novel name. Thus, the child is able to link the novel name to the correct object via the space in which her body, her attention, her actions, and the object itself occur

This work demonstrates, for instance, that children can bind the novel word to the correct object even when the experimenter points to an empty location on the table. We have also demonstrated that space is special in facilitating these mappings: Associating the potential referents with unique colors and providing the name in the presence of one of these colors does not support mapping. Furthermore, children learn words better when their parents keep objects in consistent spatial locations when teaching them. Thus, a nonobvious factor—the history of where objects have been placed in a task—matters in young children’s early word learning. This initially surprising finding fits with research showing that both adults and children will look back to the location in which a fact or sound was previously presented when trying to recall that information (Richardson & Kirkham, 2004; Richardson & Spivey, 2000). It is also consistent with the use of space for reference in sign languages and in gestural communication.

they reflect a deep challenge in trying to infer a computational-level theory from an inherently non-linear, complex, and emergent system

Emergence—the idea that behav- ior arises through the interaction of many components over time without recourse to explicit coding and without needing to be hardwired—played a key role in each of the examples we reviewed.

Hebbian learning

Thus, Hebbian learning gives rise to a new emergent ability—the ability to actively and flexibly remember a cued target location.

Fortunately, DST also provides orga- nized ways of analyzing a system and defining the scope of a research question. Specifi- cally, if components are strongly coupled, we have to care about them and their interactions as a set because they all matter for the behavior or cognition in question. If, however, components are weakly coupled, then we can study each more independently (at least within context). A “subsystem” in DST, therefore, is defined as a collection of strongly coupled components that function as an integrated system, actively resisting per- turbations from, for instance, external inputs, and showing only weak coupling to other components (Sch € oner, 1995). Thus, while scientific examination requires carving the sys- tem into analyzable subsystems, the joints used to carve a dynamic system are defined relative to the specific behavior or phenomenon under examination; they are the places where a behavioral analysis suggests weak coupling among components. Such joints are not always readily apparent. For instance, one might think that learning names for hierar- chically nested categories might be immune to perceptual-level processes, but this was not the case with the SCE.

What we have argued, however, is that the concept of emergence is fundamentally at odds with a computational-level analysis because such analyses start by defining the agent’s behavior in terms of goals that are independent of the neural, physical, and historical processes that produce behavior. This does not mean that computational-level thinking cannot still be impact- ful. It is clear that Marr’s Vision has provided great benefit to cognitive science, and that computational analyses have led to advancements in many areas of cognitive sci- ence (see papers in Perception vol. 41, 2012, for review and commentary). Neverthe- less, the specific examples reviewed here demonstrate that a dynamic systems perspective provides a valuable alternative framework for cognitive science because it appreciates that the neural-behavioral system is complex, non-linear, and emergent (see also McClelland et al., 2010)

Where do new forms of behavior and thought such as riding a bicycle or a new chess strategy come from (see also Poggio, 2012)? This is a question that other approaches to cognition typically ignore, sometimes going so far as to build in miniature pre-formed versions of new behaviors (McClelland, 2010; Smith, 2001; Smith & Pereira, 2009). In contrast, a DST perspective suggests that new behaviors can emerge organically as subtle changes in the components of the system —the strength of a muscle that stabilizes a reach, the presentation of objects sequentially in time, the association of an object with a specific location—softly assemble and pro- duce changes in cognition and action.

But does emergence require that we throw up our hands and say everything matters? No, we can (and should) be analytical about our research question. DST offers a way to determine a unit of study, that is, what a “subsys- tem” is and how to separate one subsystem from another.